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lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN

        ->and2(ulong(map 0xFF << $_, @shifts)->slice(':,*,*') )
        ->shiftright( ulong(@shifts)->slice(':,*,*') )
        ->byte;

    my $pdl_scaled = (
            # Scale to [ 0, 1 ].
            ( $pdl_channels / float(255) )
        );

    ## flip vertically to see image right way up
    #show_in_gnuplot( $pdl_channels->slice(':,:,-1:0')         ); #DEBUG
    #show_in_gnuplot(   $pdl_scaled->slice(':,:,-1:0') * 255.0 ); #DEBUG

    $pdl_scaled;
}

my @pdl_images = map {
    load_image_to_pdl(
        $images_for_test_to_uri{$_},
        $model_name_to_params{$model_name}{image_size}
    );
} @image_names;

my $pdl_image_batched = cat(@pdl_images);
my $t = FloatPDLTOTFTensor($pdl_image_batched);

p $pdl_image_batched;
p $t;

my $RunSession = sub {
    my ($session, $t) = @_;
    my @outputs_t;

    $session->Run(
        undef,
        $outputs{in}, [$t],
        $outputs{out}, \@outputs_t,
        undef,
        undef,
        $s
    );
    AssertOK($s);

    return $outputs_t[0];
};

say "Warming up the model";
use PDL::GSL::RNG;
my $rng = PDL::GSL::RNG->new('default');
my $image_size = $model_name_to_params{$model_name}{image_size};
my $warmup_input = zeros(float, 3, @$image_size, 1 );
$rng->get_uniform($warmup_input);

p $RunSession->($session, FloatPDLTOTFTensor($warmup_input));

my $output_pdl_batched = FloatTFTensorToPDL($RunSession->($session, $t));
my $softmax = sub { ( map $_/sumover($_)->dummy(0), exp($_[0]) )[0] };
my $probabilities_batched = $softmax->($output_pdl_batched);
p $probabilities_batched;

my $N = 5; # number to select

my $top_batched = $probabilities_batched->qsorti->slice([-1, -$N]);

my @top_lists   = dog($top_batched);

my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;

if( IN_IPERL ) {
    my $html = IPerl->html(
        my_table( [0..$#image_names], sub {
            my ($batch_idx, $h) = @_;
            my $image_name = $image_names[$batch_idx];
            my @top_for_image = $top_lists[$batch_idx]->list;
            (
                    $h->tt($image_name),
                    $h->a( { href => $images_for_test_to_uri{$image_name} },
                        $h->img({
                            src => $images_for_test_to_uri{$image_name},
                            alt => $image_name,
                            width => '50%',
                        })
                    ),
                    do {
                        my @tr;
                        push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
                        while( my ($i, $label_index) = each @top_for_image ) {
                            my $class_index = $includes_background_class ? $label_index : $label_index + 1;
                            push @tr, [ $h->td(
                                    $i + 1,
                                    $class_index,
                                    $labels[$class_index],
                                    $probabilities_batched->at($label_index,$batch_idx),
                            ) ];

                        }
                        $h->table([$h->tr(@tr)])
                    },
                )
        })
    );
    IPerl->display($html);
} else {
    for my $batch_idx (0..$#image_names) {
        my $image_name = $image_names[$batch_idx];
        my @top_for_image = $top_lists[$batch_idx]->list;
        my @td;
        say "Image name: `$image_name`";
        my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
        my @rows;
        while( my ($i, $label_index) = each @top_for_image ) {
            my $class_index = $includes_background_class ? $label_index : $label_index + 1;
            push @rows, [ (
                    $i + 1,
                    $class_index,
                    $labels[$class_index],
                    $probabilities_batched->at($label_index,$batch_idx),
            ) ];
        }
        say generate_table( rows => [ $header, @rows ], header_row => 1 );
        print "\n";

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN

          undef,
          $outputs{in}, [$t],
          $outputs{out}, \@outputs_t,
          undef,
          undef,
          $s
      );
      AssertOK($s);
  
      return $outputs_t[0];
  };
  
  say "Warming up the model";
  use PDL::GSL::RNG;
  my $rng = PDL::GSL::RNG->new('default');
  my $image_size = $model_name_to_params{$model_name}{image_size};
  my $warmup_input = zeros(float, 3, @$image_size, 1 );
  $rng->get_uniform($warmup_input);
  
  p $RunSession->($session, FloatPDLTOTFTensor($warmup_input));

B<STREAM (STDOUT)>:

  Warming up the model

B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
    </span><span style="color: #6666cc;">Type           </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
    </span><span style="color: #6666cc;">Dims           </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">1</span><span style=""> </span><span style="color: #ff6633;">1001</span><...
    </span><span style="color: #6666cc;">NumDims        </span><span style=""> </span><span style="color: #ff6633;">2</span><span style="">
    </span><span style="color: #6666cc;">ElementCount   </span><span style=""> </span><span style="color: #ff6633;">1001</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

Then we send the batched image data. The returned scores need to by normalised using the L<softmax function|https://en.wikipedia.org/wiki/Softmax_function> with the following formula (taken from Wikipedia):

$$ {\displaystyle \sigma (\mathbf {z} )I<{i}={\frac {e^{z>{i}}}{\sum I<{j=1}^{K}e^{z>{j}}}}\ \ {\text{ for }}i=1,\dotsc ,K{\text{ and }}\mathbf {z} =(zI<{1},\dotsc ,z>{K})\in \mathbb {R} ^{K}.} $$

  my $output_pdl_batched = FloatTFTensorToPDL($RunSession->($session, $t));
  my $softmax = sub { ( map $_/sumover($_)->dummy(0), exp($_[0]) )[0] };
  my $probabilities_batched = $softmax->($output_pdl_batched);
  p $probabilities_batched;

B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">1001 12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12012</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">2.73727380317723e-07</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">0.980696022510529</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

=head2 Results summary

Then select the top 5 of those and find their class labels.

  my $N = 5; # number to select
  
  my $top_batched = $probabilities_batched->qsorti->slice([-1, -$N]);
  
  my @top_lists   = dog($top_batched);
  
  my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;
  
  if( IN_IPERL ) {
      my $html = IPerl->html(
          my_table( [0..$#image_names], sub {
              my ($batch_idx, $h) = @_;
              my $image_name = $image_names[$batch_idx];
              my @top_for_image = $top_lists[$batch_idx]->list;
              (
                      $h->tt($image_name),
                      $h->a( { href => $images_for_test_to_uri{$image_name} },
                          $h->img({
                              src => $images_for_test_to_uri{$image_name},
                              alt => $image_name,
                              width => '50%',
                          })
                      ),
                      do {
                          my @tr;
                          push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
                          while( my ($i, $label_index) = each @top_for_image ) {
                              my $class_index = $includes_background_class ? $label_index : $label_index + 1;
                              push @tr, [ $h->td(
                                      $i + 1,
                                      $class_index,
                                      $labels[$class_index],
                                      $probabilities_batched->at($label_index,$batch_idx),
                              ) ];
  
                          }
                          $h->table([$h->tr(@tr)])
                      },
                  )
          })
      );
      IPerl->display($html);
  } else {
      for my $batch_idx (0..$#image_names) {
          my $image_name = $image_names[$batch_idx];
          my @top_for_image = $top_lists[$batch_idx]->list;
          my @td;
          say "Image name: `$image_name`";
          my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
          my @rows;
          while( my ($i, $label_index) = each @top_for_image ) {
              my $class_index = $includes_background_class ? $label_index : $label_index + 1;
              push @rows, [ (
                      $i + 1,
                      $class_index,
                      $labels[$class_index],
                      $probabilities_batched->at($label_index,$batch_idx),
              ) ];
          }
          say generate_table( rows => [ $header, @rows ], header_row => 1 );
          print "\n";